Adaptive robust control of MIMO nonlinear systems in semi-strict feedback forms

By Bin Yao, Masayoshi Tomizuka. at - Automatisierungstechnik, 2001

349 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/9ad41dd814ed9ab0c85f24ce2b1d001c56425e21

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In Control and Systems Engineering

1. Shaocheng Tong
2. Yong-ming Li
3. Shuzhi Sam Ge
4. Mou Chen
5. Yanjun Liu
6. Xiaoping Liu
7. Bing Chen
8. Peng Shi

Citations in the corpus

• INDIRECT ADAPTIVE ROBUST CONTROL OF SISO NONLINEAR SYSTEMS IN SEMI-STRICT FEEDBACK FORMS (2002, methodology): “The prediction error model (18) is obtained from the transformed tracking error dynamics, which are prone to be corrupted by neglected factors such as the measurement noises.”
• Robust Adaptive Tracking Control for Nonlinear Systems Based on Bounds of Fuzzy Approximation Parameters (2010, background): “Recently, this restriction was overcome in [24], i.”
• Adaptive Fuzzy Tracking Control Design for SISO Uncertain Nonstrict Feedback Nonlinear Systems (2016, background): “If fi(·) and gi(·) are unknown, and fi(·) satisfies that |fi(·)| ≤ φi(|x̄|), then (1) becomes the nonstrict nonlinear system studied in [26] and [27].”
• Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints (2011, background): “In Yao and Tomizuka (2001), adaptive robust control was proposed for MIMO nonlinear systems in semi-strict feedback forms.”
• Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach (2012, background): “In [30], [46], and [47], the concept of semistrict-feedback systems was proposed.”
• Saturated Adaptive Robust Control for Active Suspension Systems (2013, background): “Moreover, during the past decade, a mathematically rigorous nonlinear adaptive robust control (ARC) theory has also been developed to lay a solid foundation for the design of a new generation of controllers and observers which will help industry build modern machines of great performance and high in”
• Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities (2010, background): “Various control techniques have been proposed for both theoretical interests and practical applications including robust adaptive control [1]–[3], sliding mode control [4], [5], backstepping control [6]–[9], and intelligent control [10]–[12].”
• Adaptive neural control of uncertain MIMO nonlinear systems (2004, background): “As indicated in [29], sometimes even the presentation of MIMO forms in a meaningful manner becomes a difficult task.”